The ECAPA-TDNN model is fed with 80-dimensional log Mel filterbank energies that are mean normalized per input segment. The model parameter updates are determined by the Adam [1]} optimizer with a Cyclical Learning Rate (CLR) [2]} using a triangular policy. Training is done for 10 epochs with batches of 32 segments. The original batch is augmented in six different ways, leading to an equivalent batch size of 192. The contamination with MUSAN additive noise is done with a random Signal-to-Noise Ratio (SNR) ranging from 0 to 10 dB. Reverberation is added by convolving with a random impulse response from the aforementioned RIRs dataset.
